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Genome-wide association study-based prediction of atrial fibrillation using artificial intelligence

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dc.contributor.author김태훈-
dc.contributor.author박희남-
dc.contributor.author엄재선-
dc.contributor.author유희태-
dc.contributor.author이문형-
dc.contributor.author정보영-
dc.contributor.author권오석-
dc.contributor.author홍명희-
dc.date.accessioned2022-07-08T03:10:55Z-
dc.date.available2022-07-08T03:10:55Z-
dc.date.issued2022-01-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/188710-
dc.description.abstractObjective: We previously reported early-onset atrial fibrillation (AF) associated genetic loci among a Korean population. We explored whether the AF-associated single-nucleotide polymorphisms (SNPs) selected from the Genome-Wide Association Study (GWAS) of an external large cohort has a prediction power for AF in Korean population through a convolutional neural network (CNN). Methods: This study included 6358 subjects (872 cases, 5486 controls) from the Korean population GWAS data. We extracted the lists of SNPs at each p value threshold of the association statistics from three different previously reported ethnical-specific GWASs. The Korean GWAS data were divided into training (64%), validation (16%) and test (20%) sets, and a stratified K-fold cross-validation was performed and repeated five times after data shuffling. Results: The CNN-GWAS predictive power for AF had an area under the curve (AUC) of 0.78±0.01 based on the Japanese GWAS, AUC of 0.79±0.01 based on the European GWAS, and AUC of 0.82±0.01 based on the multiethnic GWAS, respectively. Gradient-weighted class activation mapping assigned high saliency scores for AF associated SNPs, and the PITX2 obtained the highest saliency score. The CNN-GWAS did not show AF prediction power by SNPs with non-significant p value subset (AUC 0.56±0.01) despite larger numbers of SNPs. The CNN-GWAS had no prediction power for odd-even registration numbers (AUC 0.51±0.01). Conclusions: AF can be predicted by genetic information alone with moderate accuracy. The CNN-GWAS can be a robust and useful tool for detecting polygenic diseases by capturing the cumulative effects and genetic interactions of moderately associated but statistically significant SNPs. Trial registration number: NCT02138695.-
dc.description.statementOfResponsibilityopen-
dc.formatapplication/pdf-
dc.languageEnglish-
dc.publisherBMJ Publishing Group-
dc.relation.isPartOfOPEN HEART-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHArtificial Intelligence*-
dc.subject.MESHAtrial Fibrillation / diagnosis*-
dc.subject.MESHAtrial Fibrillation / epidemiology-
dc.subject.MESHAtrial Fibrillation / genetics-
dc.subject.MESHDNA / genetics*-
dc.subject.MESHFemale-
dc.subject.MESHGenetic Predisposition to Disease*-
dc.subject.MESHGenome-Wide Association Study-
dc.subject.MESHHomeodomain Proteins / genetics*-
dc.subject.MESHHomeodomain Proteins / metabolism-
dc.subject.MESHHumans-
dc.subject.MESHMale-
dc.subject.MESHMiddle Aged-
dc.subject.MESHMorbidity / trends-
dc.subject.MESHPolymorphism, Single Nucleotide*-
dc.subject.MESHRepublic of Korea / epidemiology-
dc.subject.MESHTranscription Factors / genetics*-
dc.subject.MESHTranscription Factors / metabolism-
dc.titleGenome-wide association study-based prediction of atrial fibrillation using artificial intelligence-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Internal Medicine (내과학교실)-
dc.contributor.googleauthorOh-Seok Kwon-
dc.contributor.googleauthorMyunghee Hong-
dc.contributor.googleauthorTae-Hoon Kim-
dc.contributor.googleauthorInseok Hwang-
dc.contributor.googleauthorJaemin Shim-
dc.contributor.googleauthorEue-Keun Choi-
dc.contributor.googleauthorHong Euy Lim-
dc.contributor.googleauthorHee Tae Yu-
dc.contributor.googleauthorJae-Sun Uhm-
dc.contributor.googleauthorBoyoung Joung-
dc.contributor.googleauthorSeil Oh-
dc.contributor.googleauthorMoon-Hyoung Lee-
dc.contributor.googleauthorYoung-Hoon Kim-
dc.contributor.googleauthorHui-Nam Pak-
dc.identifier.doi10.1136/openhrt-2021-001898-
dc.contributor.localIdA01085-
dc.contributor.localIdA01776-
dc.contributor.localIdA02337-
dc.contributor.localIdA02535-
dc.contributor.localIdA02766-
dc.contributor.localIdA03609-
dc.relation.journalcodeJ04205-
dc.identifier.eissn2053-3624-
dc.identifier.pmid35086918-
dc.subject.keywordatrial fibrillation-
dc.subject.keywordgenetics-
dc.subject.keywordgenome-wide association study-
dc.contributor.alternativeNameKim, Tae-Hoon-
dc.contributor.affiliatedAuthor김태훈-
dc.contributor.affiliatedAuthor박희남-
dc.contributor.affiliatedAuthor엄재선-
dc.contributor.affiliatedAuthor유희태-
dc.contributor.affiliatedAuthor이문형-
dc.contributor.affiliatedAuthor정보영-
dc.citation.volume9-
dc.citation.number1-
dc.citation.startPagee001898-
dc.identifier.bibliographicCitationOPEN HEART, Vol.9(1) : e001898, 2022-01-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers

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